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Super-resolution and denoising of 4D-Flow MRI using physics-Informed deep neural nets
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Noise in magnitude magnetic resonance images
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Variational data assimilation for transient blood flow simulations: Cerebral aneurysms as an illustrative example
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Semiautomated method for noise reduction and background phase error correction in MR phase velocity data
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Correction of phase offset errors in main pulmonary artery flow quantification
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Phase unwrapping in 4D MR flow with a 4D single-step laplacian algorithm
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Accelerating cine phase‐contrast flow measurements using k‐t BLAST and k‐t SENSE
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Quantitative 2D and 3D phase contrast MRI: Optimized analysis of blood flow and vessel wall parameters
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Phase‐contrast magnetic resonance imaging measurements in intracranial aneurysms in vivo of flow patterns, velocity fields, and wall shear stress: Comparison with computational fluid dynamics
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Improved SNR in phase contrast velocimetry with five‐point balanced flow encoding
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SPIRiT: Iterative self‐consistent parallel imaging reconstruction from arbitrary k‐space
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Simulation of phase contrast MRI of turbulent flow
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Reconstruction of divergence‐free velocity fields from cine 3D phase‐contrast flow measurements
inferredcites →https://doi.org/10.1007/s10439-005-8772-2openalex-snapshot
Flow Imaging and Computing: Large Artery Hemodynamics
inferredcites →https://doi.org/10.1007/s10439-009-9854-3openalex-snapshot
Quantification of Hemodynamic Wall Shear Stress in Patients with Bicuspid Aortic Valve Using Phase-Contrast MRI
inferredcites →https://doi.org/10.1007/s10439-011-0447-6openalex-snapshot
In Vivo Validation of Numerical Prediction for Turbulence Intensity in an Aortic Coarctation
inferredcites →https://doi.org/10.1007/s10665-009-9266-2openalex-snapshot
Hemodynamics in normal cerebral arteries: qualitative comparison of 4D phase-contrast magnetic resonance and image-based computational fluid dynamics
inferredcites →https://doi.org/10.1007/s11886-014-0481-8openalex-snapshot
4D Flow Imaging: Current Status to Future Clinical Applications
inferredcites →https://doi.org/10.1016/0021-9991(90)90007-nopenalex-snapshot
Neural algorithm for solving differential equations
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Transient flow prediction in an idealized aneurysm geometry using data assimilation
inferredcites →https://doi.org/10.1016/j.compfluid.2019.104391openalex-snapshot
Noise reduction of flow MRI measurements using a lattice Boltzmann based topology optimisation approach
inferredcites →https://doi.org/10.1016/j.ejrad.2009.06.009openalex-snapshot
Accelerated time-resolved three-dimensional MR velocity mapping of blood flow patterns in the aorta using SENSE and k-t BLAST
inferredcites →https://doi.org/10.1016/j.jbiomech.2014.04.018openalex-snapshot
High-resolution computational fluid dynamics detects flow instabilities in the carotid siphon: Implications for aneurysm initiation and rupture?
inferredcites →https://doi.org/10.1016/j.jbiomech.2015.07.039openalex-snapshot
The effect of resolution on viscous dissipation measured with 4D flow MRI in patients with Fontan circulation: Evaluation using computational fluid dynamics
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Computational Fluid Dynamics modeling of contrast transport in basilar aneurysms following flow-altering surgeries
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Merging computational fluid dynamics and 4D Flow MRI using proper orthogonal decomposition and ridge regression
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
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Flow diverters for treatment of intracranial aneurysms: Current status and ongoing clinical trials
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Investigation of intracranial aneurysm hemodynamics following flow diverter stent treatment
inferredcites →https://doi.org/10.1016/j.mri.2019.08.028openalex-snapshot
Noise estimation for the velocity in MRI phase-contrast
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A unified deep artificial neural network approach to partial differential equations in complex geometries
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3-D Flow Reconstruction Using Divergence-Free Interpolation of Multiple 2-D Contrast-Enhanced Ultrasound Particle Imaging Velocimetry Measurements
inferredcites →https://doi.org/10.1088/0967-3334/29/5/005openalex-snapshot
Flow–area relationship in internal carotid and vertebral arteries
inferredcites →https://doi.org/10.1097/rli.0b013e3181ea3588openalex-snapshot
Blood Velocity Calculated From Volumetric Dynamic Computed Tomography Angiography
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Accounting for residence-time in blood rheology models: do we really need non-Newtonian blood flow modelling in large arteries?
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Artificial neural networks for solving ordinary and partial differential equations
inferredcites →https://doi.org/10.1109/tmi.2016.2570010openalex-snapshot
Enhancing the Velocity Data From 4D Flow MR Images by Reducing its Divergence
inferredcites →https://doi.org/10.1115/1.1468866openalex-snapshot
Noninvasive Measurement of Time-Varying Three-Dimensional Relative Pressure Fields Within the Human Heart
inferredcites →https://doi.org/10.1115/1.2970056openalex-snapshot
Numerical Simulations of Flow in Cerebral Aneurysms: Comparison of CFD Results and In Vivo MRI Measurements
inferredcites →https://doi.org/10.1115/1.4026108openalex-snapshot
Cerebral Blood Flow in a Healthy Circle of Willis and Two Intracranial Aneurysms: Computational Fluid Dynamics Versus Four-Dimensional Phase-Contrast Magnetic Resonance Imaging
inferredcites →https://doi.org/10.1115/1.4031794openalex-snapshot
The Computational Fluid Dynamics Rupture Challenge 2013—Phase II: Variability of Hemodynamic Simulations in Two Intracranial Aneurysms
inferredcites →https://doi.org/10.1136/neurintsurg-2013-011080openalex-snapshot
Association between hemodynamic conditions and occlusion times after flow diversion in cerebral aneurysms
inferredcites →https://doi.org/10.1136/neurintsurg-2014-011348openalex-snapshot
Assessment of intra-aneurysmal flow modification after flow diverter stent placement with four-dimensional flow MRI: a feasibility study
inferredcites →https://doi.org/10.1136/neurintsurg-2018-013921openalex-snapshot
Comparison of intracranial aneurysm flow quantification techniques: standard PIV vs stereoscopic PIV vs tomographic PIV vs phase-contrast MRI vs CFD
inferredcites →https://doi.org/10.1161/01.str.0000144648.89172.0fopenalex-snapshot
Magnitude and Role of Wall Shear Stress on Cerebral Aneurysm
inferredcites →https://doi.org/10.1161/01.str.17.5.913openalex-snapshot
Transcranial Doppler measurement of middle cerebral artery blood flow velocity: a validation study.
inferredcites →https://doi.org/10.1186/s12938-015-0104-7openalex-snapshot
Computational fluid dynamics simulations of blood flow regularized by 3D phase contrast MRI
inferredcites →https://doi.org/10.1186/s12968-015-0174-5openalex-snapshot
4D flow cardiovascular magnetic resonance consensus statement
inferredcites →https://doi.org/10.1227/neu.0000000000001083openalex-snapshot
Association of Hemodynamic Factors With Intracranial Aneurysm Formation and Rupture
inferredcites →https://doi.org/10.1371/journal.pone.0121051openalex-snapshot
High-Resolution Postcontrast Time-of-Flight MR Angiography of Intracranial Perforators at 7.0 Tesla
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The critical role of hemodynamics in the development of cerebral vascular disease
inferredcites →https://doi.org/10.3171/2019.4.focus19181openalex-snapshot
A review on the reliability of hemodynamic modeling in intracranial aneurysms: why computational fluid dynamics alone cannot solve the equation
inferredcites →https://doi.org/10.3174/ajnr.a2993openalex-snapshot
Point: CFD—Computational Fluid Dynamics or Confounding Factor Dissemination
inferredcites →https://doi.org/10.3174/ajnr.a3558openalex-snapshot
High WSS or Low WSS? Complex Interactions of Hemodynamics with Intracranial Aneurysm Initiation, Growth, and Rupture: Toward a Unifying Hypothesis
inferredcites →https://doi.org/10.3174/ajnr.a3901openalex-snapshot
Generalized versus Patient-Specific Inflow Boundary Conditions in Computational Fluid Dynamics Simulations of Cerebral Aneurysmal Hemodynamics
inferredcites →https://doi.org/10.3233/bir-1979-16303openalex-snapshot
Rheological parameters for the viscosity viscoelasticity and thixotropy of blood1
inferredcites →https://doi.org/10.3348/kjr.2016.17.4.445openalex-snapshot
Hemodynamic Measurement Using Four-Dimensional Phase-Contrast MRI: Quantification of Hemodynamic Parameters and Clinical Applications
inferredcites →https://doi.org/10.3978/j.issn.2223-3652.2014.01.02openalex-snapshot
4D flow imaging with MRI.
inferredcites →https://doi.org/10.48550/arxiv.1502.05700openalex-snapshot
Scalable Bayesian Optimization Using Deep Neural Networks
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Input Parameterized Physics Informed Neural Network for Advanced 4d Flow MRI Processing
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Neural Fields for Highly Accelerated 2D Cine Phase Contrast MRI
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Inverse problems in blood flow modeling: A review
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A comparison of machine learning methods for recovering noisy and missing 4D flow MRI data
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Editorial for “Segmentation of the Aorta and Pulmonary Arteries Based on 4D Flow MRI in the Pediatric Setting Using Fully Automated Multi‐Site, Multi‐Vendor, and Multi‐Label Dense U‐Net”
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Sinogram‐based flow estimation in computed tomography using a physics‐informed neural network: Impact of gantry rotation speed, X‐ray fluence and pulsed acquisition on accuracy
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Accelerated 4D ‐flow MRI with 3‐point encoding enabled by machine learning
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Quantifying physiological variability and improving reproducibility in 4D ‐flow MRI cerebrovascular measurements with self‐supervised deep learning
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Jointly Learned 3D Non‐Cartesian Sampling With Wave Encoding and Reconstruction for Neurovascular Phase Contrast MRI
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Impact of Partial Echo on 4D Flow MRI: The Insight From Synthetic MRI
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Developing and deploying deep learning models in brain magnetic resonance imaging: A review
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Magnetic Resonance Imaging-Based 4D Flow: The Role of Artificial Intelligence
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Deep Physics-Informed Super-Resolution of Cardiac 4D-Flow MRI
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Conditional Physics-Informed Graph Neural Network for Fractional Flow Reserve Assessment
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Physics-Informed Machine Learning for Industrial Reliability and Safety Engineering: A Review and Perspective
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Quantitative Comparison of Blood Flow Patterns from in Silico Simulations and 4D Flow Data Before and After Left Atrial Occlusion
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Super-Resolution and Segmentation of 4D Flow MRI Using Deep Learning and Weighted Mean Frequencies
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Design Optimization of Permanent Magnet Coupler Based on Physics-Informed Neural Networks
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Super-resolution analysis via machine learning: a survey for fluid flows
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Parameter estimation in blood flow models from highly undersampled k-space magnetic resonance imaging data
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Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging—State-of-the-Art and Challenges
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Physics-informed neural networks for hidden boundary detection and flow field reconstruction
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Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond
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Enhanced 4D Flow MRI-Based CFD with Adaptive Mesh Refinement for Flow Dynamics Assessment in Coarctation of the Aorta
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Super-Resolving and Denoising 4D flow MRI of Neurofluids Using Physics-Guided Neural Networks
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Recent developments in modeling, imaging, and monitoring of cardiovascular diseases using machine learning
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From PINNs to PIKANs: recent advances in physics-informed machine learning
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Good things come in pairs: paired autoencoders for inverse problems
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A robust shape model for blood vessels analysis
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Artificial intelligence in 4D flow MRI: Review of technological aspects and clinical applications
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A comparative study of dimensional and non-dimensional inputs in physics-informed and data-driven neural networks for single-droplet evaporation
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Deep vectorised operators for pulsatile hemodynamics estimation in coronary arteries from a steady-state prior
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Noninvasive pressure difference mapping by integrating ultrasonic vector flow imaging and physics-informed conditional variational learning
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Fluid dynamic analysis in predicting the recanalization of intracranial aneurysms after coil embolization – A study of spatiotemporal characteristics
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Volumetric emission tomography for combustion processes
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Deep learning architecture for sparse and noisy turbulent flow data
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Physics-informed neural networks for transonic flow around a cylinder with high Reynolds number
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Self-supervised learning for effective denoising of flow fields
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Towards a new paradigm in intelligence-driven computational fluid dynamics simulations
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Flow field tomography with uncertainty quantification using a Bayesian physics-informed neural network
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Physics-informed deep-learning applications to experimental fluid mechanics
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A multi-modality approach for enhancing 4D flow magnetic resonance imaging via sparse representation
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Accelerated simulation methodologies for computational vascular flow modelling
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Flow measurements in clinical cardiac imaging
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Hard-constrained neural networks for modeling nonlinear acoustics
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Segmentation-Free Super-Resolved 4D flow MRI Reconstruction Exploiting Navier-Stokes Equations and Spatial Regularization
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Navier-Stokes-Based Regularization for 4d Flow MRI Super-Resolution
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Faster 4D Flow MRI Scan with 3D Arbitrary-Scale Super-Resolution
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Generalized Super-Resolution 4D Flow MRI - Using Ensemble Learning to Extend Across the Cardiovascular System
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Segmentation-Free Velocity Field Super-Resolution on 4D Flow MRI
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Physics-Informed Neural Networks for Brain Hemodynamic Predictions Using Medical Imaging
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Deep Learning for Temporal Super-Resolution 4D Flow MRI
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Physics Informed Deep Neural Net Inverse Modeling for Estimating Model Parameters in Permeable Porous Media Flows
inferred← citeshttps://doi.org/10.1115/1.4055347openalex-snapshot
Thermal Modeling of Patient-Specific Breast Cancer With Physics-Based Artificial Intelligence
inferred← citeshttps://doi.org/10.1115/1.4065165openalex-snapshot
Unsupervised Denoising and Super-Resolution of Vascular Flow Data by Physics-Informed Machine Learning
inferred← citeshttps://doi.org/10.1115/1.4065673openalex-snapshot
A Transformative Approach for Breast Cancer Detection Using Physics-Informed Neural Network and Surface Temperature Data
inferred← citeshttps://doi.org/10.1115/1.4072290openalex-snapshot
Physics-Informed Neural Network Assisted Extraction of Height-Averaged Pulsatile Flow Velocity From Scalar Signal Transport in a Shallow Microfluidic Channel
inferred← citeshttps://doi.org/10.1117/12.2607314openalex-snapshot
Physics-informed neural networks for identification of material properties using standing waves
inferred← citeshttps://doi.org/10.1117/12.3009713openalex-snapshot
A denoising diffusion fluid flow generative model for stenotic pipe flows
inferred← citeshttps://doi.org/10.1145/3689037openalex-snapshot
Physics-Informed Computer Vision: A Review and Perspectives
inferred← citeshttps://doi.org/10.1145/3768158openalex-snapshot
Physics-informed Machine Learning for Medical Image Analysis
inferred← citeshttps://doi.org/10.1161/strokeaha.124.050540openalex-snapshot
Quantitative Ischemic Lesions of Portable Low–Field Strength MRI Using Deep Learning–Based Super-Resolution
inferred← citeshttps://doi.org/10.1186/s12968-023-00942-zopenalex-snapshot
4D Flow cardiovascular magnetic resonance consensus statement: 2023 update
inferred← citeshttps://doi.org/10.1371/journal.pone.0325644openalex-snapshot
ML-ROM wall shear stress prediction in patient-specific vascular pathologies under a limited clinical training data regime
inferred← citeshttps://doi.org/10.1609/aaai.v37i7.25988openalex-snapshot
PIXEL: Physics-Informed Cell Representations for Fast and Accurate PDE Solvers
inferred← citeshttps://doi.org/10.2139/ssrn.5119778openalex-snapshot
Mdcta-Based Computational Fluid Dynamics for the Validation and Assessment of Aortic Coarctation
inferred← citeshttps://doi.org/10.3389/fcvm.2022.1052068openalex-snapshot
Advances in machine learning applications for cardiovascular 4D flow MRI
inferred← citeshttps://doi.org/10.3389/frai.2022.928181openalex-snapshot
SRflow: Deep learning based super-resolution of 4D-flow MRI data
inferred← citeshttps://doi.org/10.3390/fluids8020046openalex-snapshot
Modeling of 3D Blood Flows with Physics-Informed Neural Networks: Comparison of Network Architectures
inferred← citeshttps://doi.org/10.3390/fluids8070196openalex-snapshot
Repaired Tetralogy of Fallot Pressure Assessment: Insights from 4D-Flow Pressure Mapping
inferred← citeshttps://doi.org/10.3990/1.9789036553193openalex-snapshot
Blood flow quantification in the aortoiliac arteries : from bench to bedside
inferred← citeshttps://doi.org/10.3990/1.9789463327480openalex-snapshot
Computational hemodynamics and vessel wall imaging for intracranial aneurysm assessment: Exploring the boundaries
inferred← citeshttps://doi.org/10.48550/arxiv.2311.11819openalex-snapshot
Generalized super-resolution 4D Flow MRI $\unicode{x2013}$ using ensemble learning to extend across the cardiovascular system
inferred← citeshttps://doi.org/10.52198/21.sti.38.cv1410openalex-snapshot
Blood Flow Quantification in Peripheral Arterial Disease: Emerging Diagnostic Techniques in Vascular Surgery
inferred← citeshttps://doi.org/10.64898/2026.09.25.754495openalex-snapshot
Biomarker-Aware Super-Resolution for 4D Flow MRI in a Synthetic Pulmonary-Artery Benchmark
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